Polarity Determination in EBSD Patterns Using the Hough Transformation

Currently, the automated electron backscatter diffraction (EBSD) technique only allows the differentiation of the Laue groups based on an electron backscatter pattern (EBSP). This article shows that information concerning the lattice plane polarity is not only stored in the EBSP, but also in the Hough transformed EBSP where it can be easily accessed for automated evaluation. Polar Kikuchi bands lead to asymmetric peaks during the Hough transformation that are dependent on the atomic number difference of the involved atoms. The effect can be strong enough to be detected when evaluating the intensities of the regular excess and deficiency lines. Polarity detection from the Hough transformation of an EBSP cannot only enhance the utility of the EBSD technique and expand the information gained from it, but also illustrates a path toward automated polarity determination during EBSD scans.

VSI Nonlinearity Compensation of a PMSM Drive System Using Deadbeat Prediction Based Current Zero-Crossing Detection

Due to the nonlinearities of the voltage-source inverter (VSI) in a permanent magnet synchronous machine (PMSM) drive system, there is always an error between the reference voltage and the actual output voltage. To compensate the voltage error, many schemes have been proposed based on the phase current polarity. However, due to factors such as current clamping, measurement noises, and control system delay, the accuracy of the detected current polarity is relatively low, especially when the current is around zero, which would therefore affect the compensation performance. To solve this issue, a deadbeat prediction-based current zero-crossing detection method (DP-CZD) is proposed in this paper. With the proposed method, the measured three-phase currents are replaced by the predicted three-phase currents in terms of the polarity determination, when the absolute value of the phase current is within the threshold range. Compared with the conventional phase current polarity detecting methods, the proposed method can greatly improve the accuracy of detected current polarity due to its smooth transient waveform, and consequently, contributes to the much higher accuracy and lower total harmonic distortion (THD) in the compensation of VSI nonlinearity, which is verified through a prototype surface-mounted PMSM.

The Variation Effect of Electric Current Toward Tensile Strength on Low Carbon Steel Welding with Electrode E7018

The strength of the welding result is strongly influenced by several factors, one of which is the selection of high current. This study aims to determine the effect of high current of welding on the strength of low carbon steel welding joints. The process of welding the material uses the open V seam connection type. The variations of the high current used were 80 A, 100 A and 130 A. The specimen used was a carbon steel plate with code of 1.0038 with thickness of 8 mm and the electrode used was the E7018 electrode with diameter of 3.2 mm. The strength of the welding results is influenced by arc voltage, amount of current, welding speed, amount of penetration and electric polarity. Determination of the amount of current in metal joints using arc welding affects the work efficiency and welding materials. Based on the research, it was found that welding using high current of 100 ampere produced the highest tensile strength value of all test specimens that were given welding treatment and good penetration results.

Comparison of Single-Trace and Multiple-Trace Polarity Determination for Surface Microseismic Data Using Deep Learning

For surface microseismic monitoring, determination of the P-wave first-motion polarity is important because (1) it has been widely used to determine focal mechanisms and (2) the location accuracy of the diffraction-stack-based method is improved greatly using polarization correction. The convolutional neural network (CNN) is a form of deep learning algorithm that can be applied to predict the polarity of a seismogram automatically. However, the existing network designed for polarity detection utilizes only individual trace information. In this study, we design a multitrace-based CNN (MT-CNN) architecture using several neighbor traces combined as training samples, which could utilize the polarity information of neighbor sensors in the surface microseismic array. We use 17,227 field seismograms with labeled polarities to train two different neural networks that predict the polarities by a single trace or by multiple traces. The performance of the test set and field example of two CNN architectures shows that the MT-CNN significantly produces fewer polarity prediction errors and leads to more accurate focal mechanism solutions for microseismic events.

Polarity Control of ZnO Films Grown on Ferroelectric (0001) LiNbO3 Substrates without Buffer Layers by Pulsed-Laser Deposition

For this study, polarity-controlled ZnO films were grown on lithium niobate (LiNbO3) substrates without buffer layers using the pulsed-laser deposition technique. The interfacial structure between the ZnO films and the LiNbO3 was inspected using high-resolution transmission electron microscopy (HR-TEM) measurements, and X-ray diffraction (XRD) measurements were performed to support these HR-TEM results. The polarity determination of the ZnO films was investigated using piezoresponse force microscopy (PFM) and a chemical-etching analysis. It was verified from the PFM and chemical-etching analyses that the ZnO film grown on the (+z) LiNbO3 was Zn-polar ZnO, while the O-polar ZnO occurred on the (-z) LiNbO3. Further, a possible mechanism of the interfacial atomic configuration between the ZnO on the (+z) LiNbO3 and that on the (-z) LiNbO3 was suggested. It appears that the electrostatic stability at the substrate surface determines the initial nucleation of the ZnO films, leading to the different polarities in the ZnO systems.

P-wave first-motion polarity determination of waveform data in western Japan using deep learning

P-wave first-motion polarity is the most useful information in determining the focal mechanisms of earthquakes, particularly for smaller earthquakes. Algorithms have been developed to automatically determine P-wave first-motion polarity, but the performance level of the conventional algorithms remains lower than that of human experts. In this study, we develop a model of the convolutional neural networks (CNNs) to determine the P-wave first-motion polarity of observed seismic waveforms under the condition that P-wave arrival times determined by human experts are known in advance. In training and testing the CNN model, we use about 130 thousand 250 Hz and about 40 thousand 100 Hz waveform data observed in the San-in and the northern Kinki regions, western Japan, where three to four times larger number of waveform data were obtained in the former region than in the latter. First, we train the CNN models using 250 Hz and 100 Hz waveform data, respectively, from both regions. The accuracies of the CNN models are 97.9% for the 250 Hz data and 95.4% for the 100 Hz data. Next, to examine the regional dependence, we divide the waveform data sets according to the observation region, and then we train new CNN models with the data from one region and test them using the data from the other region. We find that the accuracy is generally high ($${ \gtrsim }$$≳ 95%) and the regional dependence is within about 2%. This suggests that there is almost no need to retrain the CNN model by regions. We also find that the accuracy is significantly lower when the number of training data is less than 10 thousand, and that the performance of the CNN models is a few percentage points higher when using 250 Hz data compared to 100 Hz data. Distribution maps, on which polarities determined by human experts and the CNN models are plotted, suggest that the performance of the CNN models is better than that of human experts.